TTT [1]} and TENT [2]} are among recent works that have been very effective in adapting models at prediction time.
TTT [1]} uses auxiliary self-supervised tasks to train the source model.
The model is then fine-tuned (via the self-supervised sub-network) for every test instance for multiple iterations.
Using TTT for adapting a new model, one needs to modify the training process (adding self-supervised sub-network) and hence needs access to the source training data, which may not be always available.
Further, the multiple backward passes take considerable amount of time which may not be available where latency is not acceptable.
TENT [2]}, on the other hand, adapts the given trained model, without accessing the source data.
TENT assumes that the data comes in batches, with batch size usually much greater than one.
It considers an online setting, where the model adapted to the current instances (batch) is used for adapting the subsequent instances (batches), which implies that the model has information about all the test instances seen till a certain point.
Motivated by the success and limitations of TTT [1]} and TENT [2]}, we enumerate the following desirable properties of algorithms developed for the realistic, and challenging SITA protocol
